Overview

Dataset statistics

Number of variables13
Number of observations5381
Missing cells10
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory588.5 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric12

Alerts

density is highly skewed (γ1 = 38.72068724)Skewed
citric acid has 137 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-12 04:56:41.199918
Analysis finished2023-12-12 04:57:22.282326
Duration41.08 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

category
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.1 KiB
white
3986 
red
1395 

Length

Max length5
Median length5
Mean length4.481509
Min length3

Characters and Unicode

Total characters24115
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowred
2nd rowred
3rd rowred
4th rowred
5th rowred

Common Values

ValueCountFrequency (%)
white 3986
74.1%
red 1395
 
25.9%

Length

2023-12-12T10:27:22.415997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:27:22.594710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
white 3986
74.1%
red 1395
 
25.9%

Most occurring characters

ValueCountFrequency (%)
e 5381
22.3%
w 3986
16.5%
h 3986
16.5%
i 3986
16.5%
t 3986
16.5%
r 1395
 
5.8%
d 1395
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24115
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5381
22.3%
w 3986
16.5%
h 3986
16.5%
i 3986
16.5%
t 3986
16.5%
r 1395
 
5.8%
d 1395
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 24115
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5381
22.3%
w 3986
16.5%
h 3986
16.5%
i 3986
16.5%
t 3986
16.5%
r 1395
 
5.8%
d 1395
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5381
22.3%
w 3986
16.5%
h 3986
16.5%
i 3986
16.5%
t 3986
16.5%
r 1395
 
5.8%
d 1395
 
5.8%

fixed acidity
Real number (ℝ)

Distinct106
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2171065
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:22.778260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.3172676
Coefficient of variation (CV)0.18252018
Kurtosis4.5668355
Mean7.2171065
Median Absolute Deviation (MAD)0.6
Skewness1.6409533
Sum38835.25
Variance1.7351938
MonotonicityNot monotonic
2023-12-12T10:27:23.046088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 280
 
5.2%
6.6 272
 
5.1%
6.4 247
 
4.6%
7 227
 
4.2%
6.9 226
 
4.2%
6.7 217
 
4.0%
7.2 202
 
3.8%
7.1 202
 
3.8%
6.5 197
 
3.7%
6.2 181
 
3.4%
Other values (96) 3130
58.2%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 7
 
0.1%
4.8 9
 
0.2%
4.9 6
 
0.1%
5 27
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 1
< 0.1%
15 1
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 1
< 0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

Distinct189
Distinct (%)3.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.29028141
Minimum0.076961041
Maximum2.7725887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:23.337061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.076961041
5-th percentile0.14842001
Q10.20701417
median0.26236426
Q30.3435897
95-th percentile0.52176556
Maximum2.7725887
Range2.6956277
Interquartile range (IQR)0.13657554

Descriptive statistics

Standard deviation0.12555859
Coefficient of variation (CV)0.43254094
Kurtosis45.916632
Mean0.29028141
Median Absolute Deviation (MAD)0.063513406
Skewness3.3192156
Sum1561.714
Variance0.01576496
MonotonicityNot monotonic
2023-12-12T10:27:23.613291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2468600779 232
 
4.3%
0.2151113796 222
 
4.1%
0.231111721 220
 
4.1%
0.2390169005 188
 
3.5%
0.2231435513 188
 
3.5%
0.1988508587 183
 
3.4%
0.2070141694 180
 
3.3%
0.1823215568 178
 
3.3%
0.2623642645 169
 
3.1%
0.2776317366 166
 
3.1%
Other values (179) 3454
64.2%
ValueCountFrequency (%)
0.07696104114 2
 
< 0.1%
0.08157998699 1
 
< 0.1%
0.08617769624 1
 
< 0.1%
0.0953101798 6
 
0.1%
0.09984533497 4
 
0.1%
0.1043600153 9
 
0.2%
0.1088544049 3
 
0.1%
0.1133286853 31
0.6%
0.1177830357 3
 
0.1%
0.1222176327 37
0.7%
ValueCountFrequency (%)
2.772588722 1
< 0.1%
2.48490665 1
< 0.1%
0.9477893989 1
< 0.1%
0.8458682676 2
< 0.1%
0.8064758659 1
< 0.1%
0.7816158285 1
< 0.1%
0.7793248768 1
< 0.1%
0.7561219797 1
< 0.1%
0.7490548125 1
< 0.1%
0.7419373447 1
< 0.1%

citric acid
Real number (ℝ)

ZEROS 

Distinct93
Distinct (%)1.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.27228248
Minimum0
Maximum4.3751279
Zeros137
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:23.881753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.039220713
Q10.21511138
median0.27002714
Q30.33647224
95-th percentile0.44468582
Maximum4.3751279
Range4.3751279
Interquartile range (IQR)0.12136086

Descriptive statistics

Standard deviation0.14169523
Coefficient of variation (CV)0.52039791
Kurtosis240.13814
Mean0.27228248
Median Absolute Deviation (MAD)0.054915758
Skewness9.3094642
Sum1464.0629
Variance0.020077539
MonotonicityNot monotonic
2023-12-12T10:27:24.134216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2623642645 266
 
4.9%
0.2776317366 243
 
4.5%
0.2468600779 237
 
4.4%
0.39877612 232
 
4.3%
0.231111721 210
 
3.9%
0.292669614 203
 
3.8%
0.2546422184 198
 
3.7%
0.2700271372 188
 
3.5%
0.2151113796 186
 
3.5%
0.2390169005 180
 
3.3%
Other values (83) 3234
60.1%
ValueCountFrequency (%)
0 137
2.5%
0.009950330853 31
 
0.6%
0.0198026273 44
 
0.8%
0.02955880224 27
 
0.5%
0.03922071315 36
 
0.7%
0.04879016417 24
 
0.4%
0.05826890812 26
 
0.5%
0.06765864847 30
 
0.6%
0.07696104114 37
 
0.7%
0.08617769624 36
 
0.7%
ValueCountFrequency (%)
4.375127893 1
 
< 0.1%
3.9355442 1
 
< 0.1%
2.772588722 1
 
< 0.1%
2.397895273 1
 
< 0.1%
0.9783261228 1
 
< 0.1%
0.8020015855 1
 
< 0.1%
0.6931471806 6
0.1%
0.6881346387 1
 
< 0.1%
0.6471032421 1
 
< 0.1%
0.6312717768 1
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct319
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.071418
Minimum0.6
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:24.397687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.8
median2.7
Q37.5
95-th percentile14.4
Maximum78
Range77.4
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.721967
Coefficient of variation (CV)0.93109403
Kurtosis25.018274
Mean5.071418
Median Absolute Deviation (MAD)1.5
Skewness2.7941478
Sum27289.3
Variance22.296972
MonotonicityNot monotonic
2023-12-12T10:27:24.726792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 204
 
3.8%
1.6 202
 
3.8%
1.4 199
 
3.7%
1.8 195
 
3.6%
1.2 175
 
3.3%
2.2 159
 
3.0%
1.9 158
 
2.9%
2.1 152
 
2.8%
1.5 151
 
2.8%
1.7 150
 
2.8%
Other values (309) 3636
67.6%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.5%
0.9 36
 
0.7%
0.95 3
 
0.1%
1 77
1.4%
1.05 1
 
< 0.1%
1.1 127
2.4%
1.15 3
 
0.1%
1.2 175
3.3%
ValueCountFrequency (%)
78 1
< 0.1%
75 1
< 0.1%
65.8 1
< 0.1%
35 1
< 0.1%
31.6 1
< 0.1%
26.05 1
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 1
< 0.1%
20.8 2
< 0.1%

chlorides
Real number (ℝ)

Distinct220
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37459938
Minimum-2.2239801
Maximum2.4662121
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size84.1 KiB
2023-12-12T10:27:25.030373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.2239801
5-th percentile0.3036589
Q10.33619754
median0.36088261
Q30.40615481
95-th percentile0.4717694
Maximum2.4662121
Range4.6901922
Interquartile range (IQR)0.069957269

Descriptive statistics

Standard deviation0.091140031
Coefficient of variation (CV)0.24330001
Kurtosis361.73667
Mean0.37459938
Median Absolute Deviation (MAD)0.030689883
Skewness-1.8734273
Sum2015.7192
Variance0.0083065052
MonotonicityNot monotonic
2023-12-12T10:27:25.308861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3301927249 165
 
3.1%
0.3530348335 160
 
3.0%
0.3476026645 158
 
2.9%
0.3583047871 158
 
2.9%
0.3419951893 152
 
2.8%
0.360882608 148
 
2.8%
0.3634241186 143
 
2.7%
0.3361975407 143
 
2.7%
0.3684031499 142
 
2.6%
0.3239611801 139
 
2.6%
Other values (210) 3873
72.0%
ValueCountFrequency (%)
-2.223980091 1
 
< 0.1%
-2.15443469 1
 
< 0.1%
0.2080083823 1
 
< 0.1%
0.2289428485 2
 
< 0.1%
0.2351334688 1
 
< 0.1%
0.2410142264 4
0.1%
0.2466212074 3
 
0.1%
0.25198421 5
0.1%
0.2571281591 5
0.1%
0.2620741394 8
0.1%
ValueCountFrequency (%)
2.466212074 1
< 0.1%
2.410142264 1
< 0.1%
2.223980091 1
< 0.1%
0.8485557944 1
< 0.1%
0.8480926088 1
< 0.1%
0.7758402264 1
< 0.1%
0.7741753281 2
< 0.1%
0.7500740668 1
< 0.1%
0.7459035926 2
< 0.1%
0.7453039914 2
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct137
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.029548
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:25.596473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q116
median28
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.861403
Coefficient of variation (CV)0.59479425
Kurtosis9.4449255
Mean30.029548
Median Absolute Deviation (MAD)12
Skewness1.3792608
Sum161589
Variance319.0297
MonotonicityNot monotonic
2023-12-12T10:27:25.840266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 162
 
3.0%
6 151
 
2.8%
26 134
 
2.5%
15 132
 
2.5%
24 128
 
2.4%
17 126
 
2.3%
34 125
 
2.3%
31 125
 
2.3%
23 121
 
2.2%
28 117
 
2.2%
Other values (127) 4060
75.5%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 2
 
< 0.1%
3 52
 
1.0%
4 45
 
0.8%
5 112
2.1%
5.5 1
 
< 0.1%
6 151
2.8%
7 82
1.5%
8 78
1.4%
9 82
1.5%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
117 1
< 0.1%
113 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

Distinct276
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.89872
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:26.172862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q174
median116
Q3154
95-th percentile205
Maximum440
Range434
Interquartile range (IQR)80

Descriptive statistics

Standard deviation56.813652
Coefficient of variation (CV)0.49880853
Kurtosis-0.31150129
Mean113.89872
Median Absolute Deviation (MAD)40
Skewness0.063977723
Sum612889
Variance3227.791
MonotonicityNot monotonic
2023-12-12T10:27:26.406919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 54
 
1.0%
113 50
 
0.9%
122 49
 
0.9%
114 49
 
0.9%
98 48
 
0.9%
128 46
 
0.9%
117 44
 
0.8%
124 44
 
0.8%
110 44
 
0.8%
150 43
 
0.8%
Other values (266) 4910
91.2%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 4
 
0.1%
8 11
 
0.2%
9 14
0.3%
10 26
0.5%
11 22
0.4%
12 26
0.5%
13 25
0.5%
14 30
0.6%
15 30
0.6%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

SKEWED 

Distinct1003
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69384139
Minimum0.68668132
Maximum6.5161931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:27.005226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.68668132
5-th percentile0.68806428
Q10.68924458
median0.69049366
Q30.6915459
95-th percentile0.6927471
Maximum6.5161931
Range5.8295118
Interquartile range (IQR)0.0023013239

Descriptive statistics

Standard deviation0.12293832
Coefficient of variation (CV)0.17718505
Kurtosis1555.9163
Mean0.69384139
Median Absolute Deviation (MAD)0.0011423763
Skewness38.720687
Sum3733.5605
Variance0.015113832
MonotonicityNot monotonic
2023-12-12T10:27:27.338059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6891391592 61
 
1.1%
0.6917461996 61
 
1.1%
0.6912453733 54
 
1.0%
0.69194646 53
 
1.0%
0.689540685 53
 
1.0%
0.6921466802 53
 
1.0%
0.6898417236 52
 
1.0%
0.6915458992 52
 
1.0%
0.6897413874 51
 
0.9%
0.6914457339 48
 
0.9%
Other values (993) 4843
90.0%
ValueCountFrequency (%)
0.6866813219 1
< 0.1%
0.6866913867 1
< 0.1%
0.6867366771 1
< 0.1%
0.6868272518 1
< 0.1%
0.6868373152 2
< 0.1%
0.6868574416 2
< 0.1%
0.6869178183 1
< 0.1%
0.686998315 1
< 0.1%
0.6870234689 1
< 0.1%
0.6870989267 1
< 0.1%
ValueCountFrequency (%)
6.516193076 1
 
< 0.1%
5.059107948 1
 
< 0.1%
4.605170186 1
 
< 0.1%
4.290459441 1
 
< 0.1%
0.7631987303 10
0.2%
0.7124496828 1
 
< 0.1%
0.6982839647 1
 
< 0.1%
0.6949904806 1
 
< 0.1%
0.6947459019 1
 
< 0.1%
0.6947209415 2
 
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)2.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.4403021
Minimum1.3137237
Maximum1.6114359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:27.555392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.3137237
5-th percentile1.3812818
Q11.4158532
median1.4398351
Q31.4655675
95-th percentile1.5040774
Maximum1.6114359
Range0.29771225
Interquartile range (IQR)0.049714379

Descriptive statistics

Standard deviation0.03778897
Coefficient of variation (CV)0.026236836
Kurtosis0.27278208
Mean1.4403021
Median Absolute Deviation (MAD)0.025732414
Skewness0.26727001
Sum7748.8252
Variance0.0014280062
MonotonicityNot monotonic
2023-12-12T10:27:27.789004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.425515074 159
 
3.0%
1.439835128 157
 
2.9%
1.420695788 150
 
2.8%
1.423108334 146
 
2.7%
1.435084525 144
 
2.7%
1.444563269 142
 
2.6%
1.430311247 139
 
2.6%
1.432700734 136
 
2.5%
1.415853163 130
 
2.4%
1.410986974 127
 
2.4%
Other values (98) 3950
73.4%
ValueCountFrequency (%)
1.313723668 1
 
< 0.1%
1.319085611 2
 
< 0.1%
1.327075001 1
 
< 0.1%
1.332366019 2
 
< 0.1%
1.335001067 3
 
0.1%
1.340250423 1
 
< 0.1%
1.342864803 3
 
0.1%
1.345472367 1
 
< 0.1%
1.348073148 6
0.1%
1.350667183 8
0.1%
ValueCountFrequency (%)
1.611435915 2
< 0.1%
1.589235205 2
< 0.1%
1.578978705 2
< 0.1%
1.572773928 1
< 0.1%
1.570697084 1
< 0.1%
1.568615918 2
< 0.1%
1.566530411 1
< 0.1%
1.564440547 2
< 0.1%
1.562346305 2
< 0.1%
1.560247668 2
< 0.1%

sulphates
Real number (ℝ)

Distinct114
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53382916
Minimum-12
Maximum11
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size84.1 KiB
2023-12-12T10:27:28.036905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.8
Maximum11
Range23
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.26940276
Coefficient of variation (CV)0.50466101
Kurtosis1293.9706
Mean0.53382916
Median Absolute Deviation (MAD)0.08
Skewness-7.4776537
Sum2872.5347
Variance0.072577849
MonotonicityNot monotonic
2023-12-12T10:27:28.302191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 214
 
4.0%
0.46 198
 
3.7%
0.54 196
 
3.6%
0.44 184
 
3.4%
0.48 173
 
3.2%
0.38 166
 
3.1%
0.52 162
 
3.0%
0.47 161
 
3.0%
0.49 160
 
3.0%
0.45 157
 
2.9%
Other values (104) 3610
67.1%
ValueCountFrequency (%)
-12 1
 
< 0.1%
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 3
 
0.1%
0.27 10
 
0.2%
0.28 12
 
0.2%
0.29 12
 
0.2%
0.3 25
0.5%
0.31 31
0.6%
ValueCountFrequency (%)
11 1
 
< 0.1%
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.540506
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:28.503069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.1848163
Coefficient of variation (CV)0.11240602
Kurtosis-0.5264056
Mean10.540506
Median Absolute Deviation (MAD)0.9
Skewness0.55695263
Sum56718.463
Variance1.4037897
MonotonicityNot monotonic
2023-12-12T10:27:28.736962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 296
 
5.5%
9.4 269
 
5.0%
9.2 211
 
3.9%
10 206
 
3.8%
10.5 196
 
3.6%
9.8 179
 
3.3%
11 176
 
3.3%
9.3 169
 
3.1%
10.4 167
 
3.1%
10.2 160
 
3.0%
Other values (101) 3352
62.3%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 4
 
0.1%
8.5 10
 
0.2%
8.6 16
 
0.3%
8.7 48
 
0.9%
8.8 69
1.3%
8.9 58
1.1%
9 140
2.6%
9.05 1
 
< 0.1%
9.1 131
2.4%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 11
0.2%
13.9 3
 
0.1%
13.8 2
 
< 0.1%
13.7 5
0.1%
13.6 11
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

quality
Real number (ℝ)

Distinct14
Distinct (%)0.3%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.9123272
Minimum1.3862944
Maximum6.3207683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2023-12-12T10:27:28.952312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.3862944
5-th percentile1.7917595
Q11.7917595
median1.9459101
Q31.9459101
95-th percentile2.0794415
Maximum6.3207683
Range4.9344739
Interquartile range (IQR)0.15415068

Descriptive statistics

Standard deviation0.19178109
Coefficient of variation (CV)0.10028676
Kurtosis231.15011
Mean1.9123272
Median Absolute Deviation (MAD)0.13353139
Skewness10.910775
Sum10282.583
Variance0.036779987
MonotonicityNot monotonic
2023-12-12T10:27:29.135417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1.945910149 2343
43.5%
1.791759469 1774
33.0%
2.079441542 860
 
16.0%
1.609437912 210
 
3.9%
2.197224577 148
 
2.8%
1.386294361 30
 
0.6%
2.302585093 5
 
0.1%
6.320768294 1
 
< 0.1%
4.33073334 1
 
< 0.1%
5.926926026 1
 
< 0.1%
Other values (4) 4
 
0.1%
(Missing) 4
 
0.1%
ValueCountFrequency (%)
1.386294361 30
 
0.6%
1.609437912 210
 
3.9%
1.791759469 1774
33.0%
1.945910149 2343
43.5%
2.079441542 860
 
16.0%
2.197224577 148
 
2.8%
2.302585093 5
 
0.1%
4.33073334 1
 
< 0.1%
5.521460918 1
 
< 0.1%
5.846438775 1
 
< 0.1%
ValueCountFrequency (%)
6.320768294 1
 
< 0.1%
6.12249281 1
 
< 0.1%
6.073044534 1
 
< 0.1%
5.926926026 1
 
< 0.1%
5.846438775 1
 
< 0.1%
5.521460918 1
 
< 0.1%
4.33073334 1
 
< 0.1%
2.302585093 5
 
0.1%
2.197224577 148
 
2.8%
2.079441542 860
16.0%

Interactions

2023-12-12T10:27:19.398218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:41.467805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:43.600071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:45.666647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:48.492567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:51.181557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:54.107533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:01.338905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:08.074717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:12.782220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:14.973230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:17.260136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:19.564829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:41.655219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:43.751503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:45.848491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:48.668017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:51.392436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:54.589182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:01.982481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:08.536309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:12.946117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:15.140122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:17.437292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:19.752205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:41.837641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:43.931010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:46.611993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:48.912233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:51.603155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:54.885171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:02.616408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:09.055868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:13.120878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:15.319825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:17.612146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:19.906610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:42.013304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:44.083257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:46.826897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:49.105251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:51.788402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:55.117560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:03.155859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:09.530014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:13.299039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:15.478328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:17.761187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:20.093043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:42.237904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:44.283807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:47.036001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:49.343126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:51.977742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:55.696155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:03.726718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:10.008716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:13.495443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:15.665076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:17.958622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:20.286198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:42.393964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:44.434285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:47.189800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:49.551854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:52.159988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:56.854427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:04.263793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:10.443835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:13.662474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:15.807586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:18.144730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:20.469332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:42.556297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:44.620808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:47.345913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:49.834301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:52.405058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:57.552599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:04.859478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:10.957626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:13.862438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:15.971871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:18.337776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:20.621129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:42.717211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:44.817204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:47.477074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:50.019743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:52.625810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:58.261639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:05.367872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:11.440817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:14.046503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:16.443161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:18.494747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:20.785921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:42.873089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:44.999500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:47.677730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:50.237859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:52.812837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:58.845487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:05.917704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:11.861706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:14.224323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:16.609973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:18.680177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:20.964200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:43.045756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:45.196904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:47.896715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:50.499965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:53.004212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:59.644836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:06.502056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:12.241093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:14.410213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:16.770495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:18.871197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:21.147463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:43.222898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:45.343407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:48.080071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:50.699756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:53.253017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:00.162023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:07.018848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:12.413796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:14.565055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:16.925147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:19.035846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:21.332957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:43.430126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:45.527014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:48.285012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:50.979243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:26:53.723503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:00.796961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:07.602333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:12.593882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:14.775077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:17.091522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:27:19.219041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-12T10:27:21.570261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:27:21.952224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

categoryfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0red7.40.5306280.0000001.90.42358211.034.00.6920471.5062970.569.41.791759
1red7.80.6312720.0000002.60.46104425.067.00.6915461.4350850.689.81.791759
2red7.80.5653140.0392212.30.45143615.054.00.6916461.4492690.659.81.791759
3red11.20.2468600.4446861.90.42171617.060.00.6921471.4255150.589.81.945910
5red7.40.5068180.0000001.80.42171613.040.00.6920471.5062970.569.41.791759
6red7.90.4700040.0582691.60.41015715.059.00.6913461.4586150.469.41.791759
7red7.30.5007750.0000001.20.40207315.021.00.6904441.4793290.4710.02.079442
8red7.80.4574250.0198032.00.4179349.018.00.6915461.4724720.579.52.079442
9red7.50.4054650.3074856.10.41408217.0102.00.6920471.4701760.8010.51.791759
10red6.70.4574250.0769611.80.45947015.065.00.6910951.4539530.549.21.791759
categoryfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
6631white6.00.2926700.50681815.90.35830529.0164.00.6920971.4206960.508.81.945910
6632white8.60.235072NaN1.20.32396129.080.00.6887881.3737160.3611.42.079442
6633white9.80.3074850.37843610.50.33619829.083.00.6909451.3584090.3010.11.609438
6634white6.00.2926700.50681815.90.35830529.0164.00.6920971.4206960.508.86.122493
6635white7.40.2231440.31481113.50.39148729.0192.00.6918961.3862940.449.11.791759
6636white7.10.1133290.2776329.62.41014229.0162.00.6912451.4816050.419.41.791759
6637white6.00.1906200.21511112.10.36840329.0164.00.6916461.4678740.399.4NaN
6638white7.50.2662030.33647218.90.38930029.0170.00.6931471.3837910.469.01.791759
6640white7.30.1222180.27763214.4-2.15443529.0109.00.6918461.4350850.359.21.945910
6641white7.10.1133290.2776329.60.37797629.0162.00.6912451.4816050.419.41.791759